Zhanli Li

Xi'an University of Science and Technology

Papers

Total Citations

H-Index

About

Zhanli Li is a researcher at the forefront of intelligent systems and autonomous robotics, with a primary focus on safety-critical applications in hazardous environments. His work uniquely bridges cognitive modeling and practical robotics, addressing the challenge of machine alertness in underground settings. Li’s most cited paper, “Alertness Estimation Using Connection Parameters of the Brain Network” (2021, 14 citations), pioneers a novel methodology for computing vigilance in unmanned monitoring vehicles, a vital capability for underground security robots operating in dangerous, unstructured environments. He further advances computer vision with his work on “Video scene classification with complex background algorithm based on improved CNNs” (2018, 5 citations), enhancing scene understanding for applications ranging from video surveillance to medical imaging. Most recently, Li tackles the real-world deployment of autonomous systems in “Autonomous Localization and Mapping Method of Mobile Robot in Underground Coal Mine Based on Edge Computing” (2023, 4 citations), where he addresses the dual challenges of non-ideal textures and limited onboard computing resources. By integrating brain-inspired alertness models, deep learning, and edge computing, Li is laying the groundwork for safer, more intelligent robots that can operate reliably in the world’s most demanding environments.